ABSTRACT Epilepsy is a devastating neurological illness that affects 65 million people worldwide. Approximately one-third of patients affected do not respond to antiepileptic drug therapy and require a thorough diagnostic work-up. Structural neuroimaging plays a pivotal role in the diagnostic evaluation of patients with focal epilepsy, identifying visible lesions in many patients that often coincide with the seizure focus. However, 20- 40% of patients have normal-appearing MRIs and this number appears to be growing. As a result, there is increased interest in identifying subtle gray and white matter network changes on non-invasive, quantitative MRI, including structural MRI (sMRI) and diffusion tensor imaging (DTI), that can help to delineate the epileptogenic network. Unfortunately, methods for selecting optimal features from sMRI/DTI data in patients with epilepsy that can address these clinical challenges have not been developed. There are at least two major barriers that have limited progress in this field. First, sample sizes have been insufficient to develop reliable classification algorithms in patients with focal epilepsy that lead to reproducible findings. The high cost of data collection - few studies scan more than 50-60 patients - has led to underpowered studies whose findings often fail to replicate and cannot adequately model confounds. Second, high computational demands have previously limited the feasibility of using sophisticated, feature-selection (i.e., Machine Learning; ML) algorithms in clinical settings. A new, large-scale data initiative (i.e., ENIGMA-epilepsy) acquired from 24 sites world-wide is now lifting these barriers and allowing for the development and validation of innovative data-driven approaches aimed at optimizing the use of MRI data in the evaluation of epilepsy. In this grant, we will leverage data collected through ENIGMA-Epilepsy?a new, cost-effective, innovative global approach that unblocks the power logjam by merging resources, data, capital infrastructure and talents of leading epilepsy centers from 14 countries across the world (2,149 patient and 1,727 healthy control MRI/DTI datasets). We will also leverage new developments in ML (i.e., deep learning) and network-based modeling (i.e., connectome- based approaches) and test whether these novel approaches improve upon classification accuracy relative to simpler, user-driven models. Our primary aim will be to test the ability of our deep learning approach (i.e., dense neural networks) to lateralize the seizure focus. In an exploratory aim, we will test the ability of our model to predict post-operative seizure outcomes. ENIGMA's harmonized approach will allow us to test our approach in over 24 datasets, diverse in age, ethnicity, age of onset, epilepsy duration, and surgical outcomes. This R-21 application addresses NIH's call for more reproducible studies by introducing a highly- powered design, and is directly aligned with NINDS's 2014 Epilepsy Benchmarks, which encourage the identification of biomarkers for assessing or predicting treatment response in patients with epilepsy.